@article{Li2026, 
author = {Shuai Li and Dongrong Liu and Shu Li and Minghua Chen},
title = {A Materials Discovery Method Considering the Trade-Off Phenomenon in Machine Learning Prediction Capabilities between Interpolation and Extrapolation: Case Study on Multi-Objective Mg-Zn-Al Alloy Design},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {14},
keywords = {High-performance material exploration, machine learning, interpolation-extrapolation trade-off, Mg-Zn-Al alloy, dual-driven approach},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.075830},
doi = {10.32604/cmc.2026.075830},
abstract = {The exploration of high-performance materials presents a fundamental challenge in materials science, particularly in predicting properties for materials beyond the known range of target property values (extrapolation). This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning (ML) models. A new ML scheme was proposed, featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization, which demonstrated superior extrapolation prediction across multiple materials datasets. Based on this ML scheme, multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature. Subsequently, the designed alloys were validated through density functional theory calculations. Furthermore, a three-category classification strategy was summarized through the dual-driven approach combining domain knowledge and data, emphasizing their synergistic potential for materials discovery. The practical framework developed in this study provides a novel research perspective for exploring high-performance materials.}
}